The number of companies in the online video and TV streaming space is growing rapidly. In Australia for example, the internet TV advertising industry is set to grow at a compound annual growth rate of 42% from 2011 to 2016, increasing the value from $54M to $311M. Comparing averages from a few different countries, Americans watch 17.3 hours of online video per month, Britons watch 17 hours of online video per month and Australians watch 10.2 hours of online video per month. These per-person viewing averages are also forecast to grow. Such broad and sustained growth is suitable for forward thinking video streaming companies to obtain significant revenue from advertising.
On-line advertising in streaming video has been the subject of much research in recent years. For example, U.S. Pat. No. 8,145,528 concerns inserting ads in a video stream; U.S. Pat. No. 6,698,020 utilizes an ad insertion device to insert ads at the household level, U.S. Pat. No. 6,704,930 considers an infrastructure for inserting ads in digital video streams, EP 2301250 concerns an interval-based ad insertion for the delivery of video streams and inserts ads dynamically according to the actual viewing time of the content as opposed to a fixed insertion, U.S. Pat. No. 8,418,195 concerns inserting advertising in a video-on-demand system given the viewer's identity, zip code, etc., and U.S. Pat. No. 8,434,104 schedules ads dynamically using data such as viewer statistics, geographical area, demography, age group, etc.
Certain non-patent publications use a method called tensor analysis to perform a low-rank approximation/matrix completion when there are more than two attributes; see Karatzoglou, Alexandros, et al. Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. [PROCEEDINGS OF THE FOURTH ACM CONFERENCE ON RECOMMENDER SYSTEMS; ACM, 2010] and Sun, Jimeng, Dacheng Tao, and Christos Faloutsos. “Beyond streams and graphs: dynamic tensor analysis.” [PROCEEDINGS OF THE 12TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING; ACM, 2006]. But tensor analysis is computationally heavy and in general the trade-off with accuracy versus computational complexity is not very well-studied.